add

src/about.py

@@ -37,7 +37,7 @@ This leaderboard provides continuous vulnerability assessment for open-source AI
 - **Dependency Management**: Vulnerability inheritance and supply chain security
 - **Regulatory Compliance**: Compliance readiness for various frameworks
 
-Lower scores indicate fewer vulnerabilities and lower risk. The overall risk score is a weighted average of all domains, with security given higher priority.
+Lower scores indicate fewer vulnerabilities and lower risk. The Trust Score is an equal-weighted average of all five domains, providing a balanced assessment of overall library trustworthiness.
 """
 
 # Which evaluations are you running? how can people reproduce what you have?

src/display/utils.py

@@ -26,7 +26,7 @@ auto_eval_column_dict = []
 auto_eval_column_dict.append(["library_type_symbol", ColumnContent, ColumnContent("T", "str", True, never_hidden=True)])
 auto_eval_column_dict.append(["library", ColumnContent, ColumnContent("Library", "markdown", True, never_hidden=True)])
 #Scores
-auto_eval_column_dict.append(["overall_risk", ColumnContent, ColumnContent("Overall Risk ⬇️", "number", True)])
+auto_eval_column_dict.append(["overall_risk", ColumnContent, ColumnContent("Trust Score ⬇️", "number", True)])
 for task in Tasks:
     auto_eval_column_dict.append([task.name, ColumnContent, ColumnContent(task.value.col_name, "number", True)])
 # Library information

src/leaderboard/read_evals.py

@@ -104,16 +104,16 @@ class AssessmentResult:
 
     def to_dict(self):
         """Converts the Assessment Result to a dict compatible with our dataframe display"""
-        # Calculate overall risk as weighted average
+        # Calculate Trust Score as equal-weight average
         weights = {
             "license_validation": 0.2,
-            "security_assessment": 0.3,
+            "security_assessment": 0.2,
             "maintenance_health": 0.2,
             "dependency_management": 0.2,
-            "regulatory_compliance": 0.1
+            "regulatory_compliance": 0.2
         }
         
-        # Calculate overall risk - if domain is missing, use highest risk score (10)
+        # Calculate Trust Score - if domain is missing, use highest risk score (10)
         risk_sum = 0
         weight_sum = 0
         
@@ -122,7 +122,7 @@ class AssessmentResult:
             risk_sum += score * weight
             weight_sum += weight
             
-        overall_risk = risk_sum / weight_sum if weight_sum > 0 else 10
+        trust_score = risk_sum / weight_sum if weight_sum > 0 else 10
             
         data_dict = {
             "assessment_id": self.assessment_id,  # not a column, just a save name
@@ -132,7 +132,7 @@ class AssessmentResult:
             AutoEvalColumn.framework.name: self.framework,
             AutoEvalColumn.library.name: make_clickable_library(self.library_name),
             AutoEvalColumn.version.name: self.version,
-            AutoEvalColumn.overall_risk.name: overall_risk,
+            AutoEvalColumn.overall_risk.name: trust_score,
             AutoEvalColumn.license_name.name: self.license,
             AutoEvalColumn.stars.name: self.stars,
             AutoEvalColumn.last_update.name: self.last_update,

src/populate.py

@@ -34,7 +34,7 @@ def get_leaderboard_df(eval_results_path, eval_requests_path, cols, benchmark_co
                     print(f"Warning: Column '{col}' missing, adding empty column")
                     all_df[col] = 10.0  # Default to highest risk
                     
-            # Sort by overall risk score (ascending - lower is better)
+            # Sort by Trust Score (ascending - lower is better)
             if AutoEvalColumn.overall_risk.name in all_df.columns:
                 all_df = all_df.sort_values(by=[AutoEvalColumn.overall_risk.name])